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ORIGINAL PAPER
International Journal on Interactive Design and Manufacturing (IJIDeM)
https://doi.org/10.1007/s12008-023-01550-y
data analytics to construct “smart factories” that are highly
automated and networked [1]. Germany was the rst coun-
try in the world to put this idea into practice [2]. The phrase
“smart factory” was coined for the rst time in Germany.
In addition to this, it entails the utilization of digital tech-
nologies to develop production processes that are more mal-
leable and customizable [3]. Printing in three dimensions,
advanced robotics, and automation are examples of these
technologies [4]. Some benets of implementing Industry
4.0 include improved adaptability, eciency, and produc-
tion [5]. In addition to these benets, there is also a reduc-
tion in costs and improved quality control. However, some
challenges come along with adopting Industry 4.0 [6].
These challenges include the need for workers to acquire
new skills and training, integrating legacy systems with
new technology, and the risk of cybersecurity threats. The
concept of Industry 4.0 will likely continue to play a large
part in the modernization and transformation of manufac-
turing and industrial processes as technology continues to
improve [7]. This is because there is a strong correlation
1 Introduction
The Fourth Industrial Revolution (Industry 4.0) refers to
the widespread implementation of cutting-edge technolo-
gies inside conventional industrial elds. It was in Germany
that the phrase “smart factory” was rst used in 2011, and
it refers to the use of cutting-edge technology such as the
Internet of Things (IoT), articial intelligence (AI), and big
Isha Batra
isha.batra2487@gmail.com
1 upGrad Campus, upGrad Education Private Limited,
Bengaluru, India
2 School of Computer Science and Engineering, Lovely
Professional University, Phagwara, Punjab, India
3 School of Mechanical Engineering, Lovely Professional
University, Phagwara, Punjab, India
4 Centre for Supply Chain Improvement, University of Derby,
Derby, UK
Abstract
The objective of the current study is to perform a systematic review of the literature, presenting diverse research domains
for forthcoming researchers to explore and oer an understanding of Industry 4.0 and its implementation across dier-
ent industrial sectors. Data from the Scopus database has been processed using Latent Dirichlet Allocation (LDA) in the
KNIME software. The ndings reveal nine industries incorporating Industry 4.0 and investigate the current research areas
linked with Industry 4.0. This article explores the relationship between Industry 4.0 and environmental sustainability,
focusing on conceptualizing and accepting sustainable practices in diverse industries. Future implications of Industry
4.0 for environmental sustainability are discussed, along with the need for more research, greater information sharing,
and relentless innovation. In addition, it analyses the eorts of several sectors to use Industry 4.0 to achieve sustainable
goals and argues that these eorts would benet from improved collaboration, education, and legal backing. This study
represents a pioneering eort in which a Latent Dirichlet Allocation (LDA) technique is applied to the domain of Industry
4.0 and its adoption across multiple sectors, intending to forecast future research directions through an analysis of the
relationship between keywords and documents.
Keywords Industry 4.0 · Latent dirichlet allocation (LDA) · Natural language processing (NLP) · Environmental
sustainability · Konstanz information miner (KNIME)
Received: 20 July 2023 / Accepted: 16 September 2023
© The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2023
Adoption of industry 4.0 in dierent sectors: a structural review using
natural language processing
ShamneeshSharma1· ArunMalik2· ChetanSharma1· IshaBatra2· Mahender SinghKaswan3·
Jose ArturoGarza-Reyes4
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International Journal on Interactive Design and Manufacturing (IJIDeM)
between the two. This is because the concept has certain
inherent qualities of its own. Industry 4.0 is being adopted
by increasing companies to streamline operations, reduce
waste and wasted spending, and upgrade their employees’
workstations. The automobile industry already uses collab-
orative robotics and computer vision to inspect raw parts
for defects and improve quality control [8]. Another illustra-
tion of this may be seen in the proliferation of technologies
such as autonomous vehicles and smart cars, augmented
reality, and the intermediary industry. In factories, materials
are moved between workstations using automated guided
vehicles, often AGVs [9]. This brings us to our nal point.
These technologies have irrevocably altered the nature of
business and are now more critical than ever. Applying
the principles of Industry 4.0 to disparate industries like
Healthcare, agriculture, and food production can have sev-
eral positive eects, including enhanced productivity levels,
product quality, and working conditions [10]. The energy
business extensively uses big data to improve its utiliza-
tion of fossil fuels and biofuels and determine which forms
of energy can be obtained at the most competitive pricing.
Internal health care and pharmaceutical industry logistics
use various cutting-edge technologies, including automated
guided vehicles (AGVs), computer vision, 3D bioprinting,
additive manufacturing, and drones [11]. Drones equipped
with computer vision for use in eld control and hyperspec-
tral cameras for the identication of stones and plastics dur-
ing fruit and vegetable classication are just two examples
of the new technologies currently being implemented in the
agriculture and food processing industries [12]. The tech-
nologies made possible by Industry 4.0 present new oppor-
tunities for industrial development in the coming years, and
nearly every sector of the economy stands to benet from
the enhancements to production, product quality, and work-
ing conditions made possible by these innovations.
Several rationales exist to examine the degree to which
Industry 4.0 has been implemented. Stakeholders can
evaluate the assimilation of novel technologies and digi-
tal resolutions and recognize deciencies, obstacles, and
accomplishments in their implementation. Furthermore,
the potential advantages, including increased productiv-
ity, reduced expenses, enhanced quality control, rened
decision-making, and novel strategies for business admin-
istration, are emphasized. Examining impediments, such
as technological, legislative, cultural, and organizational
factors, that may impede or prevent the implementation of
Industry 4.0 is a viable undertaking. Identifying these hin-
drances allows for developing strategies, promoting col-
laboration, and providing requisite aid [13]. The gaps that
have been identied about a variety of concerns, including
but not limited to the distinct implementation obstacles that
are particular to dierent sectors, the evaluation of ROI, the
impact on the labor force and job market, the integration of
outdated systems, the ethical and societal consequences, the
participation of SMEs, and the necessity for inter-sectoral
cooperation and ecosystems. Understanding these chal-
lenges can aid in developing tailored strategies and solu-
tions to address them eectively. In addition, academic
inquiries must explore the nancial implications, possible
expense decreases, eciency improvements, and other eco-
nomic aspects linked to adopting Industry 4.0.
Industry 4.0 is an umbrella term for a suite of cutting-
edge technologies that aid in the digitalization and mechani-
zation of many sectors. Key innovations include the Internet
of Things, extensive data analysis, articial intelligence and
machine learning, advanced robots, additive manufactur-
ing, cyber-physical systems, cloud computing, augmented
and virtual reality, and cloud storage. Countries take dier-
ent approaches to implementing Industry 4.0, with major
industrial powers like Germany, the United States, and
Japan playing outsized roles. Industry 4.0 (I4.0) ideas are
widely adopted by emerging economies like China, South
Korea, and India to improve productivity, quality, and
creativity. However, many challenges must be overcome
before Industry 4.0 (I4.0) can be fully adopted by busi-
nesses. These challenges include the need for a cultural shift
towards encouraging innovation and embracing continuous
improvement; a lack of standardized practices; diculty in
interoperability; apprehensions about data security; and the
complexity of managing change. Introducing Industry 4.0
presents unrivaled opportunities for innovation, productiv-
ity, and competitiveness despite these challenges. Resolving
these issues thoroughly is essential for the long-term suc-
cess of Industry 4.0 initiatives.
Furthermore, analyzing the impact on workforce dynam-
ics and composition is imperative. Rectifying these research
inadequacies will ultimately augment understanding of the
assimilation of Industry 4.0 in heterogeneous domains,
expedite decision-making grounded on empirical substanti-
ation, and foster the eective execution of Industry 4.0 tech-
nologies across sundry industries. Given the identied gaps
in existing research, the present study utilized the Latent
Dirichlet Allocation Technique to identify potential areas
for an investigation that future researchers could pursue.
The fundamental objective of this research is to carry
out an exhaustive investigation of the reasons and aims that
have resulted in the widespread adoption of Industry 4.0.
Using techniques from natural language processing, this
study aims to analyze the existing literature and identify the
most signicant goals connected with implementing Indus-
try 4.0 across various business sectors. Having modeled the
issue and reviewed the relevant literature, the researchers
established four research questions and summarised their
ndings.
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International Journal on Interactive Design and Manufacturing (IJIDeM)
●RQ1: Identify the research contributions of Industry 4.0
in terms of publication growth, dominating authors, and
countries.
●RQ2: Identify which sectors have implemented Indus-
try 4.0 and assess how well they contribute to current
research.
●RQ3: What aspects of Industry 4.0 currently require the
most research attention?
●RQ4: What is the role of Industry 4.0 in Environmental
Sustainability?
This methodology facilitates the examination of distinct
hurdles, prospects, and implementations of Industry 4.0
technologies within particular industries, revealing perspec-
tives that could be disregarded in a broader assessment.
Through the comparative analysis of extracted topics across
diverse sectors, scholars can discern common obstacles,
practical strategies, and prospects for knowledge dissemi-
nation, thereby generating innovative perspectives on the
interrelationships among distinct sectors and the prospects
for exchanging concepts and resolutions. By incorporating
domain-specic knowledge and expert validation, the utili-
zation of LDA methodology bolsters This, in turn, facilitates
the eective transformation of research ndings into action-
able approaches for integrating and accepting Industry 4.0
technologies within diverse industries.
Researching the adoption of Industry 4.0 in various
industries is motivated by the profound changes that Indus-
try 4.0 technologies promise to bring to various industries.
The widespread adoption of this phenomenon is inuenced
by various factors, including but not limited to the trans-
formation of the workforce, legislative and regulatory
considerations, advancements in technology, enhanced pro-
ductivity, economic implications, global competitiveness,
risk mitigation strategies, and the analysis of real-world
scenarios [14]. The primary impetus for this study is the
dearth of analysis in the eld. Specically, there is a notable
absence of contemporary research that systematically analy-
ses and evaluates the current literature and research outputs
about the recognition process. The contemporary period of
articial intelligence has facilitated the utilization of many
sophisticated machine learning and deep learning models
in identication and recognition procedures, resulting in a
potentially substantial improvement in recognition accu-
racy [15]. Several methodologies exist for topic modeling,
among which Latent Dirichlet Allocation (LDA) stands out
as a widely recognized and extensively employed approach
in this domain. The review team has decided to conduct
an experiment utilising a topic modeling-based method to
extract the research trends within the domain of mathemati-
cal expression recognition.
The subsequent sections of the document are organized
in the following manner:
The paper’s second section provides an overview of the
pertinent literature, while the third section outlines the con-
ceptual framework for Industry 4.0. Section 4 pertains to
the methodology, while Sect. 5 centers on the presentation
and analysis of results. The preceding section addressed the
nal remarks of the study and potential avenues for future
research.
2 Literature review
In recent years, many individuals have pondered the con-
cept of Industry 4.0 due to its potential to improve numer-
ous industries signicantly. However, there is growing
concern that introducing technologies related to Industry
4.0 could have unintended eects on the natural world,
such as increased energy consumption, waste production,
and carbon dioxide emissions. In light of these concerns, a
substantial amount of research has been conducted into the
relationship between Industry 4.0 and the concept of envi-
ronmental sustainability. [16]. This review of the existing
research focuses on the conceptualization, drivers, and chal-
lenges, as well as the adoption of Industry 4.0 technologies
related to environmental sustainability across various indus-
tries. Many industry experts believe that the fourth indus-
trial revolution, also known as Industry 4.0, will positively
aect the environment. They believe this will occur because
it will make the utilization of resources more eective, it
will reduce waste and emissions, and improve environmen-
tal monitoring and management. However, there is a fear
that implementing technologies related to Industry 4.0 may
have unforeseen adverse eects, such as an increase in the
amount of energy consumed, the production of electronic
waste, and the risk of cyber-attacks [17]. Several studies
identied various reasons that inspire rms to implement
Industry 4.0 technologies for environmental sustainability.
These drivers include regulatory compliance, cost savings,
competitive advantage, and stakeholder pressure. In addi-
tion, these drivers push rms to adopt Industry 4.0 technol-
ogies [18]. However, the importance and weight of these
drivers may shift depending on the industry and the situa-
tion’s specics. Implementing technology related to Indus-
try 4.0 to maintain a healthy environment has obstacles [19].
Several studies have pointed out the importance of accept-
able data quality, technical skills, organizational capacities,
and stakeholder engagement to adopt and diuse these tech-
nologies [14] successfully. Protection of personal data and
intellectual property are two examples of the ethical, social,
and legal repercussions of Industry 4.0 that have been
addressed. Other repercussions include the potential for
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International Journal on Interactive Design and Manufacturing (IJIDeM)
manufacturing eld [23]. The present investigation employs
Latent Dirichlet Allocation (LDA) as a methodological
approach to scrutinize scholarly articles about Industry 4.0
within the automotive industry. The authors have identied
and classied signicant research topics, oering valuable
insights into implementing Industry 4.0 within the automo-
tive sector. One more recent study applied another topic
modeling technique LSA on Industry 4.0 [10]. The study
employs a retrospective approach through the utilisation of
text mining techniques on the Scopus database. The corpus
of 4,364 articles published between 2013 and 2023 was sub-
jected to analysis using latent semantic analysis (LSA). The
scholars produced ten distinct groupings utilizing relevant
keywords within the elds of the industrial revolution and
environmental sustainability, thereby identifying ten poten-
tial areas of research that warrant further investigation.
The Latent Dirichlet Allocation (LDA) model measures
the occurrence and signicance of themes within a given
corpus across various domains. The present investigation
emphasizes particular topics, discerns patterns, and assigns
a hierarchy of signicance. Clustering and categoriz-
ing Industry LDA facilitates 4.0 information from diverse
sectors. This facilitates organizing and retrieving crucial
information for researchers, policymakers, and industry pro-
fessionals. The utilization of LDA has the potential to unveil
Industry 4.0 applications and use cases specic to particu-
lar sectors, thereby illuminating the unique challenges,
opportunities, and success stories associated with each sec-
tor. Latent Dirichlet Allocation (LDA) has the potential to
facilitate the tracking of Industry 4.0 trends, emerging con-
cerns, and evolving research and industry practices. Several
industries have undergone signicant transformations due
to Industry 4.0 technology, specically due to increased
environmental and social sustainability. These technolo-
gies enable continuous monitoring and optimization of
resource utilization, waste reduction, energy management,
and the transition to a circular economy [24]. In addition,
they contribute to disseminating supply chain transparency,
enabling stakeholders to make informed decisions based on
an accurate assessment of environmental impact [25]. The
incorporation of robotics and automation, the adoption of
remote work practices, the promotion of skill development,
the evolution of job roles, and active community engage-
ment all contribute to enhanced social sustainability.
Nonetheless, additional concerns must be addressed in
this context [26]. This category of diculties includes the
digital divide, privacy and ethics concerns, high energy
costs, and electronic pollution [10]. Concerns regarding pri-
vacy and ethics arise from the collection and use of data,
and closing the digital divide requires equal access to tech-
nology and educational resources. Concerns have also been
increased unemployment. Recent studies indicate that most
rms are just beginning to integrate technology related to
Industry 4.0 for environmental sustainability. These studies
also reveal various adoption rates and levels among nations
and businesses [20]. However, there are also examples of
best practices and success stories that can serve as models
for other organizations and industries. These specic types
of models are applied in various elds of study. This assess-
ment of the relevant literature highlights the importance of
expanding one’s knowledge of the environmental impacts
of Industry 4.0. This is especially true when considering the
social and ethical consequences that Industry 4.0 will have
on the environment’s long-term sustainability, determining
the most eective legislative and regulatory frameworks to
promote the use of these technologies, and quantifying the
environmental impacts that these technologies will have
overall.
Utilizing Latent Dirichlet Allocation (LDA) eectively
identies research gaps about Industry 4.0 across diverse
industries. This methodology can potentially be utilized
for analyzing topic coverage, evaluating coherence, and
comparing research agendas and frameworks. By compar-
ing identied topics with established frameworks, scholars
can pinpoint areas that have not been suciently explored
or align them with pre-existing frameworks. The Latent
Dirichlet Allocation (LDA) model can reveal specialized or
nascent subjects that have not been extensively explored in
scholarly works. This can indicate potential areas of inves-
tigation where there has been a shortage of research. Inte-
grating expert knowledge with LDA can corroborate and
enhance the identied topics, furnishing supplementary per-
spectives on potential lacunae in research. The utilization of
LDA for scrutinizing extant literature on Industry 4.0 can
facilitate researchers in acquiring a more profound compre-
hension of dominant research themes, detecting underrep-
resented or developing themes, and recognizing domains
that necessitate further exploration. Integrating this analy-
sis with the insights of experts can provide valuable guid-
ance for researchers, funding agencies, and policymakers to
eectively address research gaps and steer future research
endeavors within the framework of Industry 4.0.
A systematic literature review on Industry 4.0 and related
technologies was conducted in 2019 [21]. The study used
LDA topic modeling to analyze a substantial collection
of scholarly articles on Industry 4.0 to identify important
research topics and trends across multiple industries. A fur-
ther investigation utilizes Latent Dirichlet Allocation (LDA)
to analyze articles and reports about Industry 4.0 within the
construction sector [22]. The authors investigate the criti-
cal issues and the eects of Industry 4.0 technologies on
the industry. T Zheng et al. conducted a systematic litera-
ture review and proposed a future research agenda in the
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International Journal on Interactive Design and Manufacturing (IJIDeM)
likely to break down and providing preventative mainte-
nance recommendations far before the event in issue [30].
Patterns in customer demand will be analyzed using AI,
and the technology will help enterprises modify production
schedules accordingly. The time has come to perform this
operation. Robots will soon have the potential to learn from
their environments and adjust to new circumstances thanks
to the integration of sensors and articial intelligence sys-
tems. The utilization of digital twins will make it possible
for manufacturers to simulate and rene their production
processes in a digital setting before putting them into action
in the real world. This will be made possible by the employ-
ment of digital twins. Cybersecurity will be an absolute
requirement if these systems are to maintain their reliability
and safety in the future [31]. The information technology
will play a crucial role in the fourth industrial revolution,
enabling manufacturers to design more productive, adapt-
able, and ecient operations.
4 Methodology
This article presents several hypotheses regarding the direc-
tion of future research in the elds of environmental sus-
tainability and Industry 4.0. The experimental approach is
depicted in Fig. 1, which may be found here. The methodol-
ogy of this study is built on three distinct pillars: the collect-
ing of data, the instrumentation of the data, and the analysis
of the data. In the following parts, we will discuss the opera-
tions illustrated in Fig. 1, which depicts the processes done
at various phases.
4.1 Data collection phase
Various publishers make online article publications avail-
able; these articles are then archived in various databases.
The Scopus database functions as the testing ground for
this investigation because, compared to other databases,
it is considered the most comprehensive and reliable [32].
Formulating the string according to the standards is the rst
step in retrieving the results from the database as per the
Prisma Guidelines. Industry 4.0, environment, and eco-
logical are the keywords used to make the string, and the
Scopus platform was used to run the string (TITLE-ABS-
KEY((“industry 4.0”) AND (“environment” OR “ecologi-
cal”))) to complete the study. The rst iteration yields 5,372
articles. In addition, the author used inclusion and exclusion
criteria, with only English-language articles being consid-
ered. The articles with missing data information like author,
title, year, abstract, etc, were discarded from the corpus. In
the end, 5,091 items are taken into consideration for the
experiment.
expressed regarding electronic waste or the issue of what to
do with obsolete electronics.
Research gaps Empirical evidence has indicated that
diverse industrial domains have embraced Industry 4.0, yet
certain research domains necessitate further scholarly scru-
tiny. This paper draws upon recent research on smart cities,
blockchain, digital marketing, sustainable marketing, social
media, and smart agriculture to explore the topic of Indus-
try 4.0 and its interconnections with other industries. The
authors employ a topic modeling technique, LDA, to pre-
dict research trends in this eld. Through a comprehensive
literature analysis, the authors concluded that LDA was uti-
lized for individual topics, whereas contemporary research
employed it more broadly.
The current research focuses on a Structural Evaluation of
the Adoption of Industry 4.0 in Various Sectors by leverag-
ing Natural Language Processing. It presents an innovative
methodology that uses Natural Language Processing (NLP)
to conduct a comprehensive analysis. The cross-sectoral
analysis conducted in this study oers a comprehensive
examination of the implementation of Industry 4.0 across
dierent industries. This analysis sheds light on emerging
trends and identies potential areas for further research.
A comprehensive examination of data from several busi-
nesses allows for identifying adoption patterns, barriers, and
determinants of success. The writers of this research study
consider sustainability as they investigate the relationship
between the adoption of Industry 4.0 and environmental
objectives. By employing natural language processing tech-
niques, researchers can eciently and expeditiously derive
comprehensive ndings from the existing body of literature.
3 Conceptualization of Industry 4.0
Increased use of state-of-the-art technology in all aspects
of production and industrialization characterizes the fourth
wave of the industrial revolution. The fourth stage of the
industrial revolution is characterized by this [27]. Informa-
tion technology (IT) is expected to play a signicant role
in the fourth industrial revolution (also known as Industry
4.0), especially in the areas of intelligent factories, predic-
tive maintenance, articial intelligence (AI), robots, digi-
tal twins, and cybersecurity, to name a few examples from
each of these categories [28]. Adopting Smart Factories will
allow machines, gadgets, and systems to increase produc-
tion eciency by automatically adjusting their operations.
This will be made possible thanks to the implementation of
Smart Factories [29]. Predictive maintenance will allow for
estimating when machines and other equipment are most
1 3
International Journal on Interactive Design and Manufacturing (IJIDeM)
nodes that perform various data manipulation and analysis
operations, such as data import/export, data pre-processing,
transformation, ltering, aggregation, and visualization.
Furthermore, KNIME facilitates integration with various
programming languages and tools, enabling users to utilize
their pre-existing code or integrate external libraries into
their workows. The KNIME platform oers a versatile and
user-friendly interface well-suited for various data analysis
tasks and domains, such as business analytics, scientic
research, predictive modeling, and process automation. It
supports individual users and collaborative teams, facili-
tating collaboration and reproducibility of data workows.
KNIME is widely used in academia, industry, and research
organizations as a powerful tool for data-driven analysis and
decision-making [33].
The corpus that was collected was inputted into the tool.
The initial stage involves pre-processing the data, and the
following steps were employed for pre-processing the col-
lected corpus. Table 1 displays the nodes utilized for execut-
ing the experiment via KNIME.
Nodes represented in Table 1 are used to experiment, and
nodes like POS tagger, case converter, Punctuation eraser,
stop word lter, stemming, and BOW are used in pre-pro-
cessing the corpus. Finally, BOW is created from the cor-
pus, which is further used to implement the LDA model for
topic modeling.
4.3 Latent dirichlet allocation (LDA) model
The LDA model is a prevalent topic modeling technique in
research articles. This technique facilitates the identication
Since this study is based on LDA topic modeling, it is
bound by the parameters provided by this technique. The
eectiveness of the empiric search string used to locate rel-
evant information on this website endangers the site’s users.
Even once a proper headcount has been conducted, the pos-
sibility of forgetting someone is unnerving. Each occur-
rence of a comprehensive search query has been deleted,
and all bibliographic information has been inferred. This
was required due to the suboptimal retrieval selection of the
literature corpus resulting from search term limitations, syn-
onyms, string construction, and various search engines. The
selected papers are then examined in two steps, the second
involving relevance cross-checking. However, several func-
tional studies may have been overlooked due to keyword
selection and search string limitations. Subject classication
is highly problematic due to the inherent subjectivity and
bias of the process. The study’s authors met many times to
explore how to circumvent this limitation. They began by
classifying the topics independently before combining their
eorts to get a nal categorization.
4.2 Experimentation
KNIME, also known as Konstanz Information Miner, is a
freely available open-source software tool for data analyt-
ics and integration. It is widely used by academic profes-
sionals owing to its user-friendly graphical interface [33]. It
allows users to assemble and execute data processing, anal-
ysis, and machine learning tasks through a drag-and-drop
graphical interface without requiring extensive program-
ming knowledge. KNIME oers a wide range of built-in
Fig. 1 Proposed methodology
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International Journal on Interactive Design and Manufacturing (IJIDeM)
[36]. LDA has proven to be an eective tool for understand-
ing and extracting knowledge from large textual datasets,
facilitating more ecient and focused research. The LDA
techniques are based on three input parameters. 1st are sev-
eral topics and two hyperparameters α and β.
α = 1/T, where T is the desired number of topics [37].
β = Set to 0.01.
Iterations = 1000.
Number of topics = 2, 5 and 10.
The number of topic solutions is inuenced by the
study suggested by [38]—Figure 3 displays the top 20
most frequent words from BOW with their corresponding
occurrence.
No research has yielded a denitive method for determin-
ing the optimal number of topics. However, Cao and Arun
have proposed specic parameters that can assist in iden-
tifying the ideal number of topics. Thus, this experiment
of latent topics in a corpus of documents. The Latent Dirich-
let Allocation (LDA) model assumes that each document
comprises a combination of multiple topics, and each topic
represents a probability distribution across words [34]. The
model iteratively assigns topics to words and documents,
estimating the topic distribution that best represents the
corpus. LDA has been widely used in research articles to
analyze large text corpora, discover hidden thematic struc-
tures, and identify key topics and their associations [35].
This technology nds applications in diverse elds, such
as natural language processing, information retrieval, and
social sciences. Figure 2 depicts the operational mechanism
of the LDA model.
By applying an LDA model to research articles, research-
ers can gain insights into the main themes present in the cor-
pus, identify inuential topics, analyze topic evolution over
time, perform document clustering, and support content-
based recommendation systems, among other applications
Fig. 2 LDA model
Node Description
Excel Reader It was used to import the Excel datasheet containing papers
Column Filer It was used to Filter the required columns containing a year-wise, journal-
wise, and author-wise classication of papers, along with columns containing
the Title and Abstract of the papers.
Value Counter Counts the number of values in the ltered columns
Excel writer It was used to export the processed results to an Excel sheet.
Cell Splitter Split the cells with Author names by ‘comma’ and ‘semicolon.‘
Column Combiner It was used to combine the title and abstract columns for text mining.
Strings to Document Converted the specied strings (Title & Abstract) to the document
POS Tagger It assigns a label to a word in a text that indicates its function in a sentence.
Again, there is exibility in the methods for accomplishing this.
Case Converter It was used to perform case conversions from a tokenized text. The text was
converted to lowercase.
Bag of Words (BOW)
creator
It was used to create a Bag of Words in the document
Number Filter It was used to remove/lter unwanted numbers and terms.
Punctuation Eraser Allowed to erase punctuation characters from the document
Stop word lter Filtered all terms of the input document contained in the specic stop word list
Frequency Filter Filtered terms in the given bag of words within a particular frequency value
Topic Extractor (Parallel
LDA)
This node is used to extract the topics from BOW
Table 1 Nodes used in KNIME
for experiment
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International Journal on Interactive Design and Manufacturing (IJIDeM)
increased with time. 1142 articles which are 22.43% of the
corpus, were encountered in 2022, and 338 articles were
encountered in 2023 as this data is considered till April
2023, so it might increase by the end of 2023.
The dataset used in this analysis was published in a wide
range of Scopus, WOS, ABDC, and ABS series journals and
several conference proceedings. Figure 5 displays the top
10 most inuential journals regarding the number of arti-
cles published. The analysis represents that Procedia Com-
puter Science and Procedia Manufacturing publish 2.24%
of articles. Procedia Computer Science has an H-index of
109, while Procedia Manufacturing has an H-index of 69. In
addition, 2.02% of articles are published in Sustainability,
which is taken care of by MDPI, having an H-index of 136.
Researchers write articles published in various journals
and conferences, and Fig. 6 represents the top 10 authors
who contribute to the eld. Analysis shows that Erwin
Rauch is leading the board with 27 articles, and he belongs
to the Free University of Bolzano, Bolzano, Italy, having
5081 citations and an H-index of 38, as per Google Scholar.
Countries play an essential role when there is a discus-
sion on research. Inter countries research is fundamental to
exchanging various thoughts of researchers. In this research
or from Fig. 7, the author analyses that Germany is leading
the board with 698 publications, which is 13.7% of the cor-
pus, and Italy is in 2nd position with 532 publications. Both
countries belong to European countries. India is leading the
board in 3rd position with 449 publications.
5.2 RQ2: identify which sectors have implemented
Industry 4.0 and assess how well they contribute to
current research
The widespread adoption of Industry 4.0 is a result of the
many advantages it provides. Some benets are the capac-
ity to make more informed decisions, boost productivity
and eciency, boost product quality, and allow for greater
customization [63]. These advancements simplify the
involves extracting 2, 5, and 10 topics from the bag-of-
words (BOW) model.
4.4 Topic labeling
Applying the LDA model on BOW provides critical terms in
each topic based on their relationship. Each word is assigned
some weight according to its occurrence. In Table 2, key
terms related to each topic are presented, along with this
high-loading article s related to the topic.
High-loading terms for each topic are represented in
Table 2, which are the terms that represent the cluster. These
topics are the research areas researchers worked on and
require deep insight from future researchers. All topics are
labeled manually by authors and subject matter experts.
5 Research questions and discussion
The rst research question (RQ1) is addressed through a
meta-analysis of data obtained from the Scopus database.
The study’s authors have endeavored to address the research
inquiries formulated during the preliminary research phase,
as indicated by Table 3. The topics generated from the cor-
pus using the topic labeling methodology respond to the two
inquiries, namely RQ2 and RQ3. The inquiry about RQ4
has been addressed by drawing upon existing literature that
explores the correlation between Industry 4.0 and ecological
sustainability.
5.1 RQ1: identify the research contributions
of Industry 4.0 in terms of publication growth,
dominating authors, and countries
In this study, 5,091 articles have been considered for the
experiment, and a year-wise analysis of these 5091 stud-
ies is presented in Fig. 4. The graph represents the rst
article published in 2013; after that, publications gradually
Fig. 3 Top 20 words from Corpus
with frequency
1 3
International Journal on Interactive Design and Manufacturing (IJIDeM)
lead times, and maximizing output are all made possible by
the tools of Industry 4.0. It also gives businesses an edge
in the marketplace by enhancing safety and risk manage-
ment, enhancing customer experience via customization,
responsiveness, and transparency, and reducing costs. This
automation of repetitive commercial operations, decreas-
ing the need for human labor while raising output per unit
of input. They also make it possible for data analytics, AI,
and machine learning to lter through mountains of data
in search of insights. This was previously inconceivable.
Rapidly adapting to shifts in customer demand, decreasing
Fig. 4 Year publication Analysis
Topic
Number
Keywords Topic Label Top
Article
2.1 privacy, system, blockchain, industri, task, attack, perform,
vunrab, control, collabor, assembli, result, oper, secur, data,
cybersecur
Security in
Industry 4.0 and
Blockchain
[39]
[40]
2.2 industri, technologi, digit, develop, environ, research, studi,
product, manufactur, process, manag, sustain, system, paper,
busi, model, innov, compani, chang
Adoption of digi-
tal technologies
in Industry 4.0
[41]
[42]
10.1 industri, iot, process, technologi, smart, food, environ, devic,
network, propos, applic, iiot, commun, service, quality, trac, iot,
deliver, cloud, agrifood
Industry 4.0 in
Food Industry
[43]
[44]
10.2 data, machin, process, model, industri, text, fashion, predict,
propos, environ, product, garment, wear, circular, sustain, tech-
nique, rd, design,
Industry 4.0 in
Textile Industry
[45]
[46]
10.3 build, applic, home, environ, comput, oc, wireless, requir,
sensor, perform, cit, iot, cloud, devic, smart, model, bim,
product
Industry 4.0 in
Construction
Management
[47]
[48]
10.4 industri, develop, sustain, digit, technologi, medic, economi,
environment, econom, environ, health, increas, sector, innov,
impact, car, automat
Industry 4.0 in
Healthcare
[49]
[50]
10.5 model, system, process, industri, grid, data, twin, power, envi-
ron, simul, design, control, electricit, integr, approach, engin,
architecture, energi
Industry 4.0 in
Energy Sector
[51]
[52]
10.6 industri, learn, educ, develop, student, digit, skill, environ,
technologi, research, studi, train, knowledg, competen, chang,
require, univers, innov, studi
Industry 4.0 in
Education
[53]
[54]
10.7 industri, studi, research, technologi, manag, sustain, digit,
automat, environ, busi, adopt, model, innov, compani, manufac-
tur, identi, chain
Industry 4.0 in
Manufacturing
and Production
[55]
[56]
10.8 industri, technologi, human, design, chemical, oper, medicin,
develop, process, automat, iot, machin, manufactur, interact,
simul,
Industry 4.0
in Chemical
Industry
[57]
[58]
10.9 drone, industri, agriculture, system, process, technologi, smart,
environ, farm, integr, chang, sustain, manag, crop, rain, harvest,
green
Industry 4.0
in Sustainable
Agriculture
[59]
[60]
10.10 suppl, process, industri, grid, data, manag, autonom, environ,
simul, design, control, vehicle, transport, chain,
Industry 4.0 in
Transportation
and Logistics
[61]
[62]
Table 2 Topic labeling with
article contribution
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International Journal on Interactive Design and Manufacturing (IJIDeM)
Fig. 7 Research areas
Fig. 6 Top authors
Fig. 5 Dominating journals
analysis
1 3
International Journal on Interactive Design and Manufacturing (IJIDeM)
and quality control. Virtual prototyping, digital pattern mak-
ing, and seamless design-to-production integration are all
made possible by digital technologies. Productivity, quality,
and supply chain management can all be improved with the
help of predictive maintenance and data analytics [72]. The
textile industry is changing due to Sector 4.0’s emphasis on
smart manufacturing, digitization, customization, sustain-
ability, and innovation. Smart fabrics, wearable tech, Edu-
cation, and an evolving textile industry are all supported
[73]. Programs for training and re-skilling workers give
them agency and facilitate the shift to Industry 4.0. Exam-
ples of the application of Industry 4.0 in the textile industry
are described in Table 4.
5.2.3 Industry 4.0 in construction management
The integration of novel technologies such as Build-
ing Information Modelling (BIM), the Internet of Things
(IoT) and sensors, Robotics and Automation, Augmented
Reality (AR), and Virtual Reality (VR) is transforming
the construction management industry, thereby altering its
landscape in the context of Industry 4.0. The Internet of
Things (IoT) facilitates remote monitoring, robotics, and
automation, optimizing repetitive and labor-intensive pro-
cesses. Additionally, augmented and virtual reality (AR/
VR) provides immersive and interactive experiences, while
building information modeling (BIM) enables real-time
visualization, dispute detection, and coordination. Industry
4.0 technologies have been found to enhance communica-
tion, teamwork, and decision-making processes in the con-
text of building projects [79]. Cloud computing and mobile
program improves productivity and eciency, consumer
choice, user happiness, and long-term sustainability.
5.2.1 Industry 4.0 in food industry
Industry 4.0 is transforming the food industry by intro-
ducing advanced technologies and processes that improve
eciency, safety, and sustainability. Table 3 represents
examples of how Industry 4.0 is being used in the food
industry:
The food industry is being brought into the modern era
by Industry 4.0, facilitating the spread of cutting-edge tech-
nology and processes that enhance productivity, safety, and
lifetime. When these technologies become more widespread
in the not-too-distant future, there will unquestionably be
signicant alterations in the production, distribution, and
consumption of food as a direct result of the eects of these
innovations [70].
5.2.2 Industry 4.0 in textile industry
Industry 4.0 is revolutionizing the textile sector by bring-
ing cutting-edge technology and procedures that boost pro-
ductivity, product quality, and environmental friendliness.
Improvements in productivity, manufacturing, sustainabil-
ity, and personalization in the textile industry are all results
of the advent of Industry 4.0 [71]. Examples include auto-
mation, robots, the Internet of Things (IoT), digitalization,
data analytics, predictive maintenance, customization, and
personalization. Automation, robots, and the Internet of
Things are used for material processing, cutting, stitching,
Sr. No. Use case Description
1 Precision
Agriculture
Farmers are realizing the nancial and operational benets of precision
agriculture and adopting it rapidly. Several methods are used to achieve
this goal, such as installing soil sensors, aerial crop surveys, and applying
machine learning algorithms [64].
2 Smart
Warehousing
Automation and robotics are used in smart warehouses to improve
inventory management and reduce waste. In addition, logistics may be
improved with self-driving vehicles, sensors to monitor stock levels, and
predictive analytics [65].
3Quality Control Modern sensors and analytics are used in quality control to ensure food
is safe and pure. For example, machine learning algorithms can inspect
goods for aws, while spectrometers and X-ray scanners may identify
impurities and alien items [66].
4 Traceability Using modern technology, it is possible to trace the journey of food
products from the farm to the table. For example, the distributed ledger
technology known as blockchain can monitor food items transported from
one location to another [67].
5 Food Safety Science and technology allow us to respond more quickly to food poison-
ing outbreaks and lessen the likelihood of their happening in the rst place
[68]. For example, outbreaks can be predicted and prevented with the help
of machine learning algorithms, and sensors can check food for pathogens.
6 Personalized
Nutrition
Technology can be used to produce personalized meal programs. For
example, machine learning algorithms assess data and make recommenda-
tions, and sensors track nutrition and exercise [69].
Table 3 Industry 4.0 use cases in
Food Industry
1 3
International Journal on Interactive Design and Manufacturing (IJIDeM)
industry due to the increased prevalence of these technolo-
gies in the future.
5.2.4 Industry 4.0 in healthcare
The healthcare sector utilizes Industry 4.0 to create indi-
vidualized and accurate patient treatment strategies. The
employment of articial intelligence algorithms to scruti-
nize medical data and devise customized treatment regimens
is encompassed within this domain. Industry 4.0 refers to
incorporating sophisticated technologies into manufactur-
ing and industrial operations. Implementing Industry 4.0 in
the healthcare sector facilitates enhancing healthcare service
delivery and optimizing patient outcomes. Table 6 provides
technologies enable cooperation and real-time data sharing,
while drones and aerial imaging are used for aerial imaging
and surveying. To improve the project’s eciency, produc-
tivity, safety, and sustainability, modular construction and
prefabrication techniques are used in conjunction with data
analytics and predictive analytics [80]. With the help of
these innovations, construction companies may overcome
traditional challenges, maximize resource utilization, and
speed up project delivery. Table 5 provides a full breakdown
of the many use cases.
Industry 4.0 profoundly aects the textile sector by
spreading cutting-edge technology and procedures that
boost productivity, quality, and environmental friendliness.
Therefore, there will likely be signicant shifts in the textile
Sr.
No.
Use case Description
1 Building
Information
Modeling
BIM is used to design, develop, and manage buildings and infrastructure. BIM
employs 3D modeling, AR, and VR to improve construction stakeholder collabo-
ration. This improves design accuracy, construction time, and cost [81].
2 Robotics and
automation
Bricklaying, concrete pouring, and demolition are just some of the risky and
monotonous construction jobs automated by robots and other automated gear—
the security and productivity of the workforce both benet from this [82].
3 Internet of
Things (IoT)
and sensors
Sensors and the Internet of Things are being used to track infrastructure and
machinery for maintenance. By analyzing this information, maintenance sched-
ules can be improved, faults can be predicted, and downtime can be avoided [83].
4 Drones in
Construction
Drones are increasingly utilized for inspections, progress monitoring, and sur-
veying construction projects. This boosts security, cuts down on expenses, and
quickens the building process [84].
5Augmented
and Virtual
Reality
Construction projects are being visualized and simulated using AR and VR
technology. This helps people visualize the nished product of an infrastructure
or building project before work begins, cutting down on mistakes and extra
expenses [85].
6Articial
Intelligence
(AI)
AI improves construction schedules, waste, and safety. AI algorithms can analyze
construction schedules and identify delays or cost overruns, enabling proactive
project management [86].
Table 5 Industry 4.0 use cases in
construction management
Sr.
No.
Use case Description
1 Digital Design
and Prototyping
Making 3D models of textile products using software tools is an integral part
of digital design and prototyping. As a result, artists may build and test proto-
types more quickly and eciently [74].
2 Smart textiles Smart textiles use sensors and electronics to make interactive and responsive
clothing. This comprises apparel that monitors vital signs, adapts to ambient
circumstances, and communicates with other devices [75].
3 Digital Printing Fabrics can have graphics printed on them digitally, utilizing inkjet printing
technology. This reduces waste and increases printing speed and accuracy [76].
4Automation and
Robotics
Machines can be programmed to perform repetitive operations such as cutting,
stitching, and packaging as part of automation and robotics. This enhances
quality and consistency while decreasing labor expenses [11].
5 Predictive
Maintenance
Predictors and analytics are employed in predictive maintenance to determine
when repairs are needed. This results in fewer breakdowns and a longer lifes-
pan for the machinery [77].
6 Supply Chain
Optimization
Optimization of the supply chain can be achieved with the use of data analytics
and machine learning algorithms. Predictive analytics for demand forecasting
and blockchain for enhanced transparency and traceability are two examples
[78].
Table 4 Industry 4.0 use cases in
textile industry
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International Journal on Interactive Design and Manufacturing (IJIDeM)
5.2.7 Industry 4.0 in manufacturing and production
The fourth industrial revolution is revolutionizing the
manufacturing sector by implementing cutting-edge tech-
nologies such as articial intelligence (AI), the Internet of
Things (IoT), and big data analytics. This has resulted in
the development of a “smart factory,“ which is a factory in
which machines and equipment connect with one other and
human operators to increase quality control and optimize
production processes [105]. The Fourth Industrial Revolu-
tion (IR4) has profoundly impacted the manufacturing and
industrial industry [106]. It has brought cutting-edge tech-
nology and digitization, causing traditional manufacturing
processes to transform and making it possible to achieve
higher levels of eciency, productivity, and personalization
[107]. The following are some of the essential characteris-
tics of Industry 4.0 regarding manufacturing and production
outlined in Table 9.
5.2.8 Industry 4.0 in chemical industry
The chemicals industry uses Industry 4.0 to improve safety
and quality control, represented in Table 10. For example,
IoT sensors and predictive maintenance prevent accidents
and equipment failures.
5.2.9 Industry 4.0 in sustainable agriculture
Industry 4.0 improves crop yields, reduces waste, and
increases sustainability in agriculture, as shown in Table 11.
This involves using precision agriculture technologies, such
as sensors and drones, to collect data on soil moisture, crop
health, and other factors, which can be used to optimize
crop management practices and improve overall eciency.
examples of the application of Industry 4.0 in healthcare use
cases.
There is a signicant opportunity for research to be con-
ducted due to implementing Industry 4.0 technologies in
Healthcare. Some examples of this include the creation of
new algorithms and machine learning models to improve
clinical decision-making and patient outcomes; the devel-
opment of wearable devices and sensors to monitor patient
health in real-time; the development of new robotic systems
for use in Healthcare; the investigation of the use of block-
chain technology to improve data security and interoper-
ability in Healthcare; and the creation of new tele-hearing
technologies. In the coming years, research in this eld will
probably play an essential part in developing new technol-
ogy and services related to Healthcare.
5.2.5 Industry 4.0 in energy sector
Industry 4.0 is used in energy to improve eciencies and
reduce waste. For instance, implementing smart grids is
improving both the distribution and consumption of energy.
In addition, using renewable energy sources such as wind
and solar power is helping to reduce dependency on fossil
fuels. Table 7 contains a discussion of some of the possible
applications.
5.2.6 Industry 4.0 in education
The eld of Education has also been signicantly impacted
by Industry 4.0, leading to what is often called “Education
4.0” or “Smart Education”. Some key aspects of Industry
4.0 in Education are described in Table 8.
Sr.
No.
Use case Description
1 Wearable
Technology
IoT and wearable technologies provide remote patient monitoring, real-time health
monitoring, and lifestyle data collection. Some examples include Fitbit, smart
watches, and medical sensors [87].
2Automatic
medical
histories
and clinical
outcomes
Big data analytics interprets patients’ and healthcare systems’ large data sets.
Medical history, demographics, and clinical outcomes are examples. These nd-
ings improve patient care, tailor treatment plans, and identify system ineciencies
[88].
3Clinical Deci-
sion Making
AI is used in clinical decision-making, predictive analytics, and personalized
medicine. For example, machine learning algorithms can predict results, custom-
ize treatment programs, and identify high-risk individuals [89].
4 Robotic care
and Remote
Assistance
Repetitive chores, surgical procedures, and remote aid for healthcare personnel
are all areas where robotics is used. Robots that can dispense medication, treat
wounds, and aid with rehabilitation are included [90].
5 Telehealth Through the use of Telehealth, patients from all around the world can have access
to healthcare services, including remote consultations and virtual care. Videocon-
ferencing, remote monitoring, and mobile health apps fall under this category [91].
Table 6 Industry 4.0 use cases in
healthcare
1 3
International Journal on Interactive Design and Manufacturing (IJIDeM)
Sr.
No.
Use case Description
1 Personalized
Learning
Industry 4.0 allows adaptive learning platforms and AI to personalize student
learning. AI algorithms can analyze student data and customize material,
pacing, and evaluation to their requirements, strengths, and limitations [99].
2 Digital Content and
Open Educational
Resources (OER)
Industry 4.0 has increased digital content and OER use. Online resources,
multimedia, e-books, and interactive learning elements can improve Educa-
tion. Open educational resources encourage collaboration and information
exchange [100].
3Online and
Blended Learning
Industry 4.0 has helped spread online learning platforms and blended
learning models, which combine face-to-face training with digital tools and
resources. These methods enable self-paced, collaborative, and distant learn-
ing [101].
4Internet of Things
(IoT) and Smart
Classrooms
IoT sensors and gadgets can track student interactions, classroom climate,
and resource consumption. Interactive whiteboards, digital projectors, and
other technology promote participation, collaboration, and interactive learn-
ing in smart classrooms [102].
5Virtual and
Augmented Real-
ity (VR/AR) for
better Learning
Experiences
VR and AR enable participatory learning. Students can learn and practice in
virtual surroundings, experiments, and scenarios. Remote collaboration and
virtual eld visits improve learning options [103].
6Data Analytics and
Learning Analytics
Industry 4.0 collects and analyses massive educational data. Learning analyt-
ics can reveal student performance, engagement, and behavior patterns.
These insights help educators personalize instruction, make data-driven deci-
sions, and support students [104].
Table 8 Industry 4.0 use cases in
education
Sr. No. Use case Description
1 Smart Grids Industry 4.0 has allowed smart grids, which use digital communication and
automation to increase energy distribution eciency, dependability, and sus-
tainability. Advanced sensors, meters, and monitoring systems in smart grids
capture real-time energy use, grid performance, and environmental data [92].
Data optimizes energy management, demand response, and grid stability.
2 Renew-
able Energy
Integration
Industry 4.0 made renewable energy grid integration easier. For example,
IoT devices, data analytics, and predictive modeling enable real-time solar
panel and wind turbine monitoring and control. This integration encourages
renewable energy use and a more sustainable balance [93].
3 Energy Storage
and Management
Industry 4.0 has advanced grid-scale batteries and smart storage systems.
These devices eciently store and manage renewable energy, lowering fos-
sil fuel use and grid stability. In addition, real-time data, demand patterns,
and pricing signals optimize energy storage, delivery, and consumption in
intelligent energy management systems [94].
4 Internet of
Things (IoT)
and Energy
Management
Industry 4.0 energy management requires IoT devices. Smart meters, sen-
sors, and linked devices provide monitoring, control, and automation for
remote energy systems. Industrial operations, buildings, and residences can
save energy with IoT devices [95].
5Energy E-
ciency and
Demand
Response
Real-time data analytics, AI algorithms, and machine learning identify and
implement energy-saving options. Digital demand response programs reduce
peak load and improve grid reliability by adjusting energy use based on
demand and pricing signals [96].
6 Cyber security
and Resilience
Industry 4.0 emphasizes cyber security and Resilience as the energy sector
becomes increasingly linked and digital. Cyber security is necessary to
protect critical energy infrastructure and data transmission and storage. In
addition, industry 4.0 encourages resilient energy systems that can survive
and recover from interruptions and calamities [97].
7 Decentralized
Energy Systems
Decentralized energy systems have grown due to Industry 4.0. For example,
digital technologies can combine rooftop solar panels and tiny wind turbines
into the grid [98].
Table 7 Industry 4.0 use cases in
energy sector
1 3
International Journal on Interactive Design and Manufacturing (IJIDeM)
need more attention using the topic modeling technique.
These areas are discussed below:
5.3.1 Adoption of digital technologies in Industry 4.0
The Industry 4.0 trend encompasses various digital tech-
nologies businesses utilize, such as the Internet of Things
(IoT), big data analytics, and articial intelligence (AI). The
Internet of Things facilitates the interconnection of various
devices, including but not limited to machines and sensors
[132]. The accessibility of real-time monitoring, data analy-
sis, and decision-making is enhanced. Enhance the qual-
ity control measures, optimize the production processes,
anticipate the maintenance requirements, and facilitate
data-driven decision-making by leveraging analytics con-
ducted on extensive datasets. The fourth industrial revolu-
tion, known as Industry 4.0, heavily relies on using articial
intelligence (AI) and machine learning (ML) technologies.
This is due to their ability to enable machines to learn and
enhance their performance independently, thus emulating
human cognitive abilities. Because they make it possible
5.2.10 Industry 4.0 in transportation and logistics
The transportation and logistics industry is expected to
experience signicant impacts from the fourth industrial
revolution, which will introduce transformative opportuni-
ties for improved automation, eciency, and optimization.
Table 12 enumerates several practical applications of Indus-
try 4.0 within the transportation and logistics sectors.
The transportation and logistics industry may use Indus-
try 4.0’s capabilities to improve overall eciency, optimize
operations, increase visibility, and lower operating costs.
Companies can adapt to the changing demands of the mar-
ket, their customers’ expectations, and their business’s regu-
latory requirements when they use modern technologies.
5.3 RQ3: what aspects of Industry 4.0 currently
require the most research attention?
In the realm of Industry 4.0, several areas have been garner-
ing signicant attention from researchers. In this research,
the researchers have identied three critical areas which
Sr.
No.
Use case Description
1Automation and
Robotics
Robotics and intelligent machines have automated manufacturing in Industry
4.0. Robots using sensors and AI can do repetitive jobs quickly and accurately,
improving productivity, worker safety, and accuracy [108]. In addition, col-
laborative robots (cobots) improve eciency and human-robot collaboration.
2 Internet of
Things (IoT) and
connectivity
The IoT connects machines, devices, and systems in Industry 4.0. Intercon-
nected machines, sensors, and industrial equipment provide real-time data col-
lection and interchange. Connectivity enables industrial process monitoring,
control, optimization, predictive maintenance, and smart factories [109].
3 Big Data and
Analytics
Industry 4.0 collects and analyses massive production data. Advanced analyt-
ics and machine learning algorithms can use this data to optimize processes,
quality control, predictive maintenance, and supply chain management. In
addition, real-time data analytics supports proactive decision-making and
improvement [28].
4Additive Manu-
facturing (3D
Printing)
The credit for the evolution of 3D Printing goes to Industry 4.0. 3D printers
eciently and wasteless to build complex and customized things [110]. This
technology allows rapid prototyping, on-demand production, and elaborate
designs that were previously dicult to make.
5 Cyber-Physical
Systems
Cyber-physical systems (CPS) connect the digital and physical worlds in
Industry 4.0. Real-time communication, control, and optimization are possible
with CPS. In addition, sensors, actuators, embedded systems, and control
systems integrate digital and physical production environments [111].
6Digital Twin and
Simulation
Industry 4.0 encourages digital twins—virtual copies of existing assets,
processes, and systems. Digital twins simulate manufacturing processes for
optimization, troubleshooting, and predictive maintenance. Manufacturers can
anticipate problems by constructing a virtual depiction [112].
7 Supply Chain
and Logistics
Optimization
Industry 4.0 improves supply chain and logistics through connectivity and
data exchange. Supply chain agility is enabled by real-time inventory, produc-
tion, and demand visibility [113]. In addition, autonomous cars and drones
improve material handling, warehousing, and transportation by reducing lead
times [114].
8 Workforce
Transformation
Industry 4.0 requires new skills and jobs. For example, workers require data
analysis, programming, maintenance, and AI cooperation skills as automation
and digitalization expand [115]. While monotonous jobs are automated, work-
ers focus on more challenging duties, problem-solving, and decision-making.
Table 9 Industry 4.0 use cases in
manufacturing and production
1 3
International Journal on Interactive Design and Manufacturing (IJIDeM)
helps businesses save time and money by streamlining pro-
cesses and eliminating repetitive tasks.
Regarding material waste, production times, and costs,
additive manufacturing, often known as 3D Printing, is
for machines to use predictive analytics, improve the pro-
duction process, and make their own decisions, AI and ML
are the driving forces behind Industry 4.0. Robotic process
automation (RPA) is one type of automation technology that
Sr.
No.
Use case Description
1 Precision
Agriculture
Industry 4.0 allows precision agriculture using sensors, drones, satellite
imagery, and GPS to collect real-time data on soil conditions, crop health, and
weather patterns [121]. This data-driven technique helps farmers accurately
manage irrigation, fertilization, and pesticide application, decreasing resource
waste, crop yields, and environmental impacts.
2 Internet of
Things (IoT) and
connectivity
Soil moisture sensors, weather stations, and animal monitoring systems
provide agricultural IoT data collection. Interconnected devices give farmers
real-time data and remote control. Connectivity improves decision-making,
problem identication, and work automation [122].
3 Smart Farming
and Robotics
Industry 4.0 automates and roboticized farming. Autonomous robots can
eciently plant, harvest, and monitor crops. These robots can use AI algo-
rithms and sensors to detect and eradicate weeds or plant health issues without
chemicals. Smart farming minimizes labor, boosts output, and promotes
sustainability [123].
4Vertical Farm-
ing and Indoor
Agriculture
Industry 4.0 facilitates indoor and vertical agriculture. LED lighting, hydropon-
ics, and climate control optimize growing conditions while reducing resource
consumption. In addition, vertical farming conserves arable land, water, and
pesticides and permits agricultural production throughout the year [124].
5 Farm Manage-
ment Systems
Industry 4.0 advocates farm management systems centralizing meteorologi-
cal, soil, and crop data. These systems help farmers manage their operations
[125]. They can manage resource usage, analyze performance indicators, and
optimize farming for sustainability and protability.
6 Farm-to-
Consumer
Traceability
Industry 4.0 improves agricultural supply chain traceability, giving consumers
information about food origin, production, and sustainability. RFID tags, QR
codes, and the blockchain allow food safety, quality, and sustainability to be
tracked from farm to consumer [126].
Table 11 Industry 4.0 use cases
in sustainable agriculture
Sr.
No.
Use case Description
1Process Opti-
mization and
Automation
Industry 4.0 automates and intelligently optimizes chemical manufacture.
Advanced sensors, control systems, and data analytics provide real-time process
parameter monitoring, control, and optimization [63]. This boosts manufactur-
ing eciency, energy eciency, product quality, and safety.
2 Internet
of Things
(IoT) and
connectivity
The IoT connects equipment, devices, and systems in the chemical sector. Con-
nected sensors and equipment measure chemical process temperature, pressure,
ow rates, and composition. This data allows remote monitoring, predictive
maintenance, and real-time process optimization [116].
3Advanced
Materials
and Additive
Manufacturing
Industry 4.0 promotes innovative chemical materials. For example, advanced
chemical techniques can synthesize materials with increased strength, exibility,
or conductivity. In addition, additive manufacturing, including 3D Printing, can
precisely build complicated structures and customized chemical products [117].
4 Safety and Risk
Management
Industry 4.0 improves chemical industry safety and risk management. Real-time
sensors and monitoring systems identify dangerous conditions, leaks, and aber-
rant process parameters. Early intervention and prevention ensure worker safety
and reduce accident risk—simulation and predictive analytics aid risk assess-
ment and mitigation [118].
5 Sustainable
Manufacturing
and Circular
Economy
Industry 4.0 promotes chemical industry sustainability. Digitalization optimizes
resources, waste, and energy. Smart sensors and automation systems can reduce
pollutants, optimize resource usage, and recycle and reuse chemicals. In addi-
tion, industry 4.0 helps chemical manufacturers use renewable energy [119].
6 Supply Chain
Optimization
Industry 4.0 improves chemical supply chain management. Real-time inventory,
production, and demand data allow agile supply chain planning and execution.
In addition, automation and analytics improve logistics, transportation, and
inventory management, reducing lead times and supply chain eciency [120].
Table 10 Industry 4.0 use cases
in chemical industry
1 3
International Journal on Interactive Design and Manufacturing (IJIDeM)
required [139]. In addition, blockchain technology provides
a decentralized identity management system, increasing
the security level oered in the Fourth Industrial Revolu-
tion. Because every user in a blockchain-based solution has
a distinct and veriable identity, the risk of identity theft,
hacking, and other cybercrime is eliminated. This distrib-
uted strategy increases the level of security provided by
Industry 4.0 by making it simpler to identify and respond to
intrusions. Furthermore, platforms for sharing data based on
blockchain technology make it easier to maintain informa-
tion security, comply with privacy regulations, and generate
audit trails for data access and utilization [137].
On the other hand, scalability, ecacy, and governance
must be considered during the implementation process.
The network consensus processes, data protection needs,
and integration with existing systems are all important con-
siderations when determining whether blockchain technol-
ogy is appropriate for a company’s goals. The capacity of
blockchain technology to deliver decentralized, transparent,
and tamper-resistant solutions in areas such as data integ-
rity, supply chain management, identity management, and
automation demonstrates the technology’s promise to boost
security and trust in Industry 4.0 [140]. This potential can
be seen in the technology’s ability to provide solutions in
these domains.
5.4 RQ4: what is the role of Industry 4.0 in
environmental sustainability?
Industry 4.0 contributes signicantly to environmental
protection by providing several chances to boost resource
eciency, lessen adverse environmental eects, and spread
environmentally friendly practices throughout industries.
Sustainable agriculture encompasses a wide range of prac-
tices, including food production, supply chain optimization,
waste management, energy savings, precision agricul-
ture, and the use of the circular economy [141]. Resource
changing the game in the manufacturing business [133].
Augmented Reality (AR), Virtual Reality (VR), and Cloud
Computing are all functional in training, displaying, and
maintaining information [134]. Cloud computing makes
it easier than ever to work with remote teams, share les,
and tap into shared data and applications [135]. Data shar-
ing is also made easier with cloud computing. To implement
Industry 4.0, robust protocols, encryption methods, access
restrictions, and threat warning systems are necessary. By
streamlining communication, data analysis, automation, and
optimization, these digital tools facilitate the development
and rollout of Industry 4.0 [136]. This facilitates connectiv-
ity, data-driven decision-making, automation, and optimiza-
tion. As a result, overall production productivity, eciency,
and originality are boosted.
5.3.2 Security in Industry 4.0 and blockchain
Due to the signicance of automation, data exchange, and
networked devices, the protection of user data and the
integrity of the system are of the uttermost importance
in the context of Industry 4.0. In the context of Industry
4.0, researchers are continually looking for new ways to
strengthen cybersecurity measures, develop robust authen-
tication and encryption technologies, and resolve privacy
concerns. It’s possible that the technology behind block-
chain can help alleviate some of the Fourth Industrial Revo-
lution’s security worries [137]. It is a decentralized ledger
that can be depended on to store information securely and
verify its authenticity [138]. Because of this, it will be more
dicult for hackers to steal information or modify les.
Because of its complete end-to-end visibility and traceabil-
ity, blockchain makes supply chain management more e-
cient by safely tracking items, components, and materials.
Because of automation and smart contracts, contracts may
automatically carry out their terms, processes may be com-
pleted more quickly, and intermediaries may no longer be
Sr.
No.
Use case Description
1 Smart Fleet
Management
Industry 4.0 technology, such as Internet of Things (IoT) sensors, GPS track-
ing, and telematics, enables real-time vehicle monitoring, increasing eet
eciency, enhancing route planning, and decreasing fuel usage [127].
2Autonomous
Vehicles
Self-driving vehicles, such as trucks and delivery drones, have the potential
to entirely revolutionize the logistics industry due to their ability to eliminate
human error, increase safety, and increase delivery speed and eciency [128].
3 Supply Chain
Visibility
Goods may be tracked and traced in real-time as they make their way through
the supply chain thanks to the Internet of Things (IoT), RFID tags, and block-
chain technology [129].
4 Smart
Warehousing
Industry 4.0 technologies such as automated storage and retrieval systems
(AS/RS), robotic picking, and articial intelligence-driven inventory manage-
ment enable intelligent warehousing solutions to be implemented [130].
5 Smart Ports and
Hubs
Modernization of ports and logistics hubs is made possible by Industry 4.0
technologies such as automated cargo handling, advanced container monitor-
ing, and digital platforms for optimal stakeholder collaboration [131].
Table 12 Industry 4.0 use cases
in transportation and logistics
1 3
International Journal on Interactive Design and Manufacturing (IJIDeM)
Implications of the study The deployment of Latent Dirich-
let Allocation (LDA) for the investigation of Industry 4.0
across various sectors bears signicant implications for
stakeholders and the ecient assimilation of Industry 4.0
technology. The analysis of Industry 4.0 implementation
in various industries has signicant implications for poli-
cymakers, practitioners, and researchers. This technology’s
exible functionality incorporates various sectors, includ-
ing policy formulation, incentive program creation, educa-
tion and training program alignment, and the advancement
of sustainability initiatives. Professionals can use This
research’s ndings eectively to develop and implement
strategic initiatives, proactively address risks, optimize
operational processes, and cultivate a collaborative envi-
ronment within their respective elds. Researchers in the
eld of articial intelligence can identify voids in existing
knowledge, allowing theoretical frameworks to advance.
In addition, they can enhance research procedures, thereby
enhancing the ecacy of scientic studies.
Moreover, these researchers possess the knowledge
required to validate existing theories and models, thereby
strengthening the foundations of scientic comprehension.
The investigation has the potential to promote cross-sectoral
knowledge dissemination and stimulate innovation. This
study contributes signicantly to the eld of Industry 4.0 by
providing vital insights that have the potential to enhance
the seamless and long-term integration of advanced technol-
ogies across numerous industries. This is accomplished by
providing benecial guidance to decision-makers, develop-
ing strategic plans, and recommending prospective research
areas. This study has the potential to considerably contrib-
ute to the successful integration of Industry 4.0 technolo-
gies, resulting in long-lasting and practical outcomes, by
incorporating empirical ndings. As previously elucidated,
the assimilation of information can enlighten the evolution
of domain-specic policies, allocation of resources, and
formulation of decision-making protocols. Consequently, it
is plausible to posit that this particular phenomenon may
engender a notable progression in economic growth, inno-
vation, and competitiveness.
6 Findings of the research
The current study stands out for its unique and original
approach, as it explores the application of Latent Dirich-
let Allocation (LDA) in the context of Industry 4.0, an area
that has not been extensively examined in existing academic
literature.
As mentioned earlier, the methodology has signicant
promise within the eld of topic modeling, since it adeptly
optimization involves using resources like energy, water,
and raw materials most eciently. In contrast, waste reduc-
tion involves producing less rubbish while recovering as
much of the resource that was used as possible. A compre-
hensive plan for increasing energy eciency should include
elements such as smart grids, energy monitoring systems,
and controls that are up to date with the latest technological
advancements [142]. A more open and transparent supply
chain that can track its components is crucial to a sustain-
able economy. Data is used to inform decisions in precision
agriculture, which enables more eective use of resources
and less use of chemicals [143]. The eciencies that can
be gained in infrastructure, transportation, and public ser-
vices due to the use of Industry 4.0 technologies benet the
growth of cities sustainably. Utilizing smart grids, informa-
tion technology systems, and data analytics can improve
many aspects of city life, including energy use, trac ow,
and the distribution of city resources [144]. The develop-
ment of sensors, remote sensing technology, and data ana-
lytics has made signicant strides in recent years, enabling
improved environmental monitoring and risk management.
Additionally, the Internet of Things contributes to
increased awareness on the part of consumers and stimu-
lates benecial behavioral shifts. Industry 4.0 is essential
in advancing environmentally sustainable practices since
it optimizes resource utilization, reduces waste, increases
energy eciency, creates sustainable supply chains, and
makes well-informed decisions easier. Industries can transi-
tion to more environmentally friendly practices by employ-
ing cutting-edge technologies and digitization, creating a
greener and more sustainable future.
The challenges impeding the extensive implementa-
tion of Industry 4.0 encompass scal implications, limited
availability of skilled workforce, apprehensions surround-
ing data condentiality, and reluctance towards embracing
novel technological advancements. Prominent attributes
observed within thriving industries encompass the resolute
dedication exhibited by their leadership, the ecacy of col-
laborative eorts, and the embrace of an agile methodology.
The healthcare and retail sectors exhibit a lower perfor-
mance level than the manufacturing and logistics sectors,
which are considered the frontrunners. The optimization of
adoption rates can be eectively facilitated through the stra-
tegic promotion of synergistic collaboration between public
and private entities, coupled with the reasonable allocation
of resources towards advancing comprehensive training
programs and enhancing infrastructure development. The
facilitation of Industry 4.0 adoption can be eectively pro-
moted by strategically implementing governmental policies
and nancial support.
1 3
International Journal on Interactive Design and Manufacturing (IJIDeM)
realized without technological advancements and solutions.
There are signicant opportunities for resource eciency
and waste reduction due to the integration of the circular
economy. This research has the potential to contribute to the
advancement of NLP methodology by providing a compre-
hensive analysis of the widespread adoption of Industry 4.0.
The ndings of this study may prove helpful in directing
future research eorts, highlighting gaps in our understand-
ing, and illuminating emerging trends. Decision-makers
can use the results of this research to promote new ideas,
overcome challenges, and forward the cause of sustainable
development. The analysis of industry adoption techniques
and the facilitation of cross-sectoral learning are two areas
that can be explored. The review provides valuable data-
driven insights that may be utilized to help decision-mak-
ing based on evidence. The ndings of this study have the
potential to provide valuable guidance for future research
endeavors. Additionally, utilizing a methodological rene-
ment approach has promise for stimulating additional
investigation and facilitating the development of practical
applications.
Declarations
Conicts of interests/Competing interests The authors have NO ali-
ations with or involvement in any organization or entity regarding any
nancial interest (such as honoraria; educational grants; participation
in speaker’s bureaus; membership, employment, consultancies, stock
ownership, or expert testimony or patent-licensing arrangements), or
non-nancial interests (such as personal or professional relationships,
aliations, knowledge or beliefs) in the subject matter or material dis-
cussed in this manuscript.
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